615 research outputs found

    Ultrafast electron diffraction using an ultracold source

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    We present diffraction patterns from micron-sized areas of mono-crystalline graphite obtained with an ultracold and ultrafast electron source. We show that high spatial coherence is manifest in the visibility of the patterns even for picosecond bunches of appreciable charge, enabled by the extremely low source temperature (~ 10 K). For a larger, ~ 100 um spot size on the sample, spatial coherence lengths > 10 nm result, sufficient to resolve diffraction patterns of complex protein crystals. This makes the source ideal for ultrafast electron diffraction of complex macromolecular structures such as membrane proteins, in a regime unattainable by conventional photocathode sources. By further reducing the source size, sub-um spot sizes on the sample become possible with spatial coherence lengths exceeding 1 nm, enabling ultrafast nano-diffraction for material science.Comment: 5 pages, 4 figure

    A Solvable Model of Secondary Structure Formation in Random Hetero-Polymers

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    We propose and solve a simple model describing secondary structure formation in random hetero-polymers. It describes monomers with a combination of one-dimensional short-range interactions (representing steric forces and hydrogen bonds) and infinite range interactions (representing polarity forces). We solve our model using a combination of mean field and random field techniques, leading to phase diagrams exhibiting second-order transitions between folded, partially folded and unfolded states, including regions where folding depends on initial conditions. Our theoretical results, which are in excellent agreement with numerical simulations, lead to an appealing physical picture of the folding process: the polarity forces drive the transition to a collapsed state, the steric forces introduce monomer specificity, and the hydrogen bonds stabilise the conformation by damping the frustration-induced multiplicity of states.Comment: 24 pages, 14 figure

    Hierarchical Self-Programming in Recurrent Neural Networks

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    We study self-programming in recurrent neural networks where both neurons (the `processors') and synaptic interactions (`the programme') evolve in time simultaneously, according to specific coupled stochastic equations. The interactions are divided into a hierarchy of LL groups with adiabatically separated and monotonically increasing time-scales, representing sub-routines of the system programme of decreasing volatility. We solve this model in equilibrium, assuming ergodicity at every level, and find as our replica-symmetric solution a formalism with a structure similar but not identical to Parisi's LL-step replica symmetry breaking scheme. Apart from differences in details of the equations (due to the fact that here interactions, rather than spins, are grouped into clusters with different time-scales), in the present model the block sizes mim_i of the emerging ultrametric solution are not restricted to the interval [0,1][0,1], but are independent control parameters, defined in terms of the noise strengths of the various levels in the hierarchy, which can take any value in [0,\infty\ket. This is shown to lead to extremely rich phase diagrams, with an abundance of first-order transitions especially when the level of stochasticity in the interaction dynamics is chosen to be low.Comment: 53 pages, 19 figures. Submitted to J. Phys.

    Video-Assisted Thoracoscopic Surgery in Patients With Clinically Resectable Lung Tumors

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    To investigate the feasibility of thoracoscopic resection, a pilot study was performed in patients with clinically resectable lung tumors. In 40 patients, Video-assisted thoracic surgery (VATS) was performed because of suspicion of malignancy. There were 29 men and 11 women with a median age of 54.8 years (range 18 to 78). Preoperative indications were suspected lung cancer and tumor in 27 patients, assessment of tumor resectability in 7 patients, and probability of metastatic tumors in 6 patients. The final diagnoses in the 27 patients with suspected lung cancer were 12 primary lung cancers, 6 lung metastases, and 9 benign lesions. The success rates for VATS (no conversion to thoracotomy) were 1 of 12 (8.3%) for resectable stage I lung cancer, 8 of 12 (66.7%) for metastatic tumors, and 9 of 9 (100%) for benign tumors. With VATS, 6 of 7 patients (85.7%), possible stage III non-small cell lung cancer, an explorative thoracotomy with was avoided, significantly reducing morbidity. The reasons for conversion to thoracotomy were 1) oncological (N2 lymph node dissection and prevention of tumor spillage) and 2) technical (inability to locate the nodule, central localization, no anatomical fissure, or poor lung function requiring full lung ventilation). The ultimate diagnoses were 19 lung cancers, 12 metastatic lung tumors, and 9 benign lung tumors. Our data show the limitations of VATS for malignant tumors in general use. These findings, together with the fact that experience in performing thoracoscopic procedures demonstrates a learning curve, may limit the use of thoracoscopic resection as a routine surgical procedure, especially when strict oncological rules are respected

    Randomized controlled trial comparing magnetic marker localization (MaMaLoc) with wire-guided localization in the treatment of early-stage breast cancer

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    Wire-guided localization (WGL) is the standard of care in the surgical treatment of nonpalpable breast tumors. In this study, we compare the use of a new magnetic marker localization (MaMaLoc) technique to WGL in the treatment of early-stage breast cancer patients. Open-label, single-center, randomized controlled trial comparing MaMaLoc (intervention) to WGL (control) in women with early-stage breast cancer. Primary outcome was surgical usability measured using the System Usability Scale (SUS, 0-100 score). Secondary outcomes were patient reported, clinical, and pathological outcomes such as retrieval rate, operative time, resected specimen weight, margin status, and reoperation rate. Thirty-two patients were analyzed in the MaMaLoc group and 35 in the WGL group. Patient and tumor characteristics were comparable between groups. No in situ complications occurred. Retrieval rate was 100% in both groups. Surgical usability was higher for MaMaLoc: 70.2 ± 8.9 vs. 58.1 ± 9.1, p < 0.001. Patients reported higher overall satisfaction with MaMaLoc (median score 5/5) versus WGL (score 4/5), p < 0.001. The use of magnetic marker localization (MaMaLoc) for early-stage breast cancer is effective and has higher surgical usability than standard WGL

    Polynomial iterative algorithms for coloring and analyzing random graphs

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    We study the graph coloring problem over random graphs of finite average connectivity cc. Given a number qq of available colors, we find that graphs with low connectivity admit almost always a proper coloring whereas graphs with high connectivity are uncolorable. Depending on qq, we find the precise value of the critical average connectivity cqc_q. Moreover, we show that below cqc_q there exist a clustering phase c[cd,cq]c\in [c_d,c_q] in which ground states spontaneously divide into an exponential number of clusters. Furthermore, we extended our considerations to the case of single instances showing consistent results. This lead us to propose a new algorithm able to color in polynomial time random graphs in the hard but colorable region, i.e when c[cd,cq]c\in [c_d,c_q].Comment: 23 pages, 10 eps figure

    Slowly evolving geometry in recurrent neural networks I: extreme dilution regime

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    We study extremely diluted spin models of neural networks in which the connectivity evolves in time, although adiabatically slowly compared to the neurons, according to stochastic equations which on average aim to reduce frustration. The (fast) neurons and (slow) connectivity variables equilibrate separately, but at different temperatures. Our model is exactly solvable in equilibrium. We obtain phase diagrams upon making the condensed ansatz (i.e. recall of one pattern). These show that, as the connectivity temperature is lowered, the volume of the retrieval phase diverges and the fraction of mis-aligned spins is reduced. Still one always retains a region in the retrieval phase where recall states other than the one corresponding to the `condensed' pattern are locally stable, so the associative memory character of our model is preserved.Comment: 18 pages, 6 figure

    On the freezing of variables in random constraint satisfaction problems

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    The set of solutions of random constraint satisfaction problems (zero energy groundstates of mean-field diluted spin glasses) undergoes several structural phase transitions as the amount of constraints is increased. This set first breaks down into a large number of well separated clusters. At the freezing transition, which is in general distinct from the clustering one, some variables (spins) take the same value in all solutions of a given cluster. In this paper we study the critical behavior around the freezing transition, which appears in the unfrozen phase as the divergence of the sizes of the rearrangements induced in response to the modification of a variable. The formalism is developed on generic constraint satisfaction problems and applied in particular to the random satisfiability of boolean formulas and to the coloring of random graphs. The computation is first performed in random tree ensembles, for which we underline a connection with percolation models and with the reconstruction problem of information theory. The validity of these results for the original random ensembles is then discussed in the framework of the cavity method.Comment: 32 pages, 7 figure
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